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E-raamat: Advanced Analytics for Industry 4.0: Traditional Industries [Taylor & Francis e-raamat]

(The University of Queensland, Australia)
  • Formaat: 332 pages, 16 Tables, black and white; 61 Line drawings, black and white; 18 Halftones, black and white; 79 Illustrations, black and white
  • Ilmumisaeg: 17-Jul-2025
  • Kirjastus: CRC Press
  • ISBN-13: 9781003186823
  • Taylor & Francis e-raamat
  • Hind: 180,03 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 257,19 €
  • Säästad 30%
  • Formaat: 332 pages, 16 Tables, black and white; 61 Line drawings, black and white; 18 Halftones, black and white; 79 Illustrations, black and white
  • Ilmumisaeg: 17-Jul-2025
  • Kirjastus: CRC Press
  • ISBN-13: 9781003186823

This volume covers the analytics revolution in Industry 4.0 for the mother industries like mining, oil & gas, steel and so forth. It fousses on use of advanced analytics and artificial intelligence to improve the business decisions aimed to increase the quality and quantity of mother industries' products.



The evolution of modern technology has affected all the industry dimensions. Mother industries play a critical role in providing the precursor materials for other industries, and a small improvement in these can make a big change in other ones. This volume covers the analytics revolution in Industry 4.0 for the mother industries like mining, oil and gas, steel and so forth. It focusses on use of advanced analytics and artificial intelligence to improve the business decisions aimed to increase the quality and quantity of mother industries' products. It helps to design and implement their digital transformation strategies in these industries.

  • Provides a concise overview of state of the art for mother industries executives and managers
  • Highlights and describes critical opportunity areas for industries operations optimization
  • Explains how to implement advanced data analytics through case studies and examples
  • Provides approaches and methods to improve data-driven decision making
  • Brings experience and learning in digital transformation from adjacent sectors

This volume is aimed at researchers, professionals, and graduate students in data science, manufacturing, automation, and computer engineering

Chapter 1: Navigating the Fourth Industrial Revolution: The Advent of
Advanced Analytics in Traditional Industries
Chapter 2: Transforming Mining
Operations: Harnessing Advanced Analytics for Optimal Decision-Making
Chapter
3: Designing Intelligence: Harnessing Soft Sensors and Advanced Analytics in
Petroleum Refining for Industry 4.0
Chapter 4: Harnessing the Convergence of
Information Technology and Operational Technology for Digital Transformation:
An Integrated Framework for Effective Project Management, Skill Development,
Team Coordination, and Collaboration in Manufacturing Industry
Chapter 5:
Harnessing Industrial Internet of Things: Enabling Artificial Intelligence
and Machine Learning for Optimized Industrial and Manufacturing Processes
Chapter 6: Digitizing the Palate: Exploring Opportunities for Digital
Transformation in the Food Industry
Chapter 7: Constructing Tomorrow:
Exploring the Future of Construction in the Era of Industry 4.0
Chapter 8:
Leveraging Advanced Analytics for Transforming Logistics: The Road to
Logistics 4.0
Chapter 9: Revolutionizing Chemical Engineering 4.0: Artificial
Intelligence Innovations and Machine Learning
Chapter 10: Harvesting
Tomorrow: The Future of Agriculture in Industry 4.0
Chapter 11: Artificial
Intelligence in Insurance: Transforming Risk Management and Customer
Experience
Ali Soofastaei is the global projects leader at Vale Artificial Intelligence Centre.Vale is a multinational corporation engaged in metals and mining. It is one of the worlds foremost producers of iron ore and the largest producer of nickel. Dr. Soofastaei leads innovative industrial projects in artificial intelligence (AI) applications to improve safety, productivity, and energy efficiency and reduce maintenance costs. He completed his Ph.D. at the University of Queensland in the field of AI applications in mining engineering, where he led a revolution in the use of deep learning and AI methods to increase energy efficiency, reduce operation and maintenance costs, and reduce greenhouse gas emissions in surface mines.